While x-rays have significant value for diagnosing the condition of a patient, ionizing X-ray radiation can be harmful to living tissue. Accordingly, with the intent of reducing radiation risks wherever possible, the International Commission on Radiological Protection (ICRP), beginning in 1977, has proposed that a policy of ALARA (As Low As Reasonably Achievable) be adopted for radiological personnel and, more recently, for patients who undergo x-ray imaging.
To address this problem, manufacturers and users of x-ray equipment have expended efforts in developing both threshold settings and procedural techniques that help to reduce exposure levels. For example, technique charts that provide recommended exposure settings for various conditions can be developed to meet the ALARA objective. These reduced settings may then be used for system tools that help to control dose levels, such as automatic exposure control (AEC), and anatomical programmed radiography (APR).
While exposure reduction is a worthwhile goal, however, its implementation should not compromise the capabilities that radiological imaging systems offer to the diagnostician. Incorrectly reducing X-ray exposure levels may result in poor quality images with reduced diagnostic value. Images produced with too little exposure can be characterized by problems such as excessive graininess and low contrast. Such images may be difficult to use and could potentially compromise diagnosis. In some cases, problems such as these require images to be re-taken.
One solution for defining the exposure level that minimizes patient exposure without compromising diagnostic image quality is reduced-dose image simulation. Advantages of simulation over other approaches are 1) generation of an image without additional exposure to the patient, 2) exploration of a range of exposure levels without risk of compromised diagnosis, and 3) evaluation of numerous patient types and pathologies.
One proposed solution for reduced-dose image simulation in fluoroscopy, described in U.S. Pat. No. 5,396,531 entitled “Method of Achieving Reduced Dose X-Ray Fluoroscopy by Employing Statistical Estimation of Poisson Noise” by Hartley, relates to an interactive method for dose adjustment. This method calculates a dose level for each successive fluoroscopic image in a series based on a given signal-to-noise (S/N) ratio. In the method, image noise power spectrum (NPS) is assumed to be proportional to intensity (exposure) and to be spatial-frequency independent. However, this simplified noise model does not appear to adequately characterize noise, which is considered to have a more complex relationship to exposure and to spatial frequency in many cases, as described subsequently in the detailed description of the invention.
A proposed solution for reduced-dose image simulation in Computed Tomography (CT) imaging has been described in the article entitled “Noise simulation in x-ray CT” by Parinaz Massoumzadeh, Orville A. Earl, and Bruce R. Whiting, in Proceedings of SPIE, Volume 5745, Medical Imaging 2005: Physics of Medical Imaging, pp. 898-909. As with the method of Hartley, this method asserts that noise is spatial frequency independent and does not characterize image noise in more general cases.
Other proposed solutions use image simulation techniques to identify suitable levels for reduced exposure under given conditions. One example method is described in the paper entitled “Potential for lower absorbed dose in digital mammography: A JAFROC experiment using clinical hybrid images with simulated dose reduction” by Pontus Timberg, Mark Ruschin, Magnus Bath, Bengt Hemdal, Ingvar Andersson, Soren Mattsson, Dev Chakraborty, Rob Saunders, Ehsan Samei, and Anders Tingberg, in Proceedings of SPIE, Volume 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 614614 (Mar. 17, 2006). In this method, low dose images are simulated from high-dose images by scaling the high dose image and adding an amount of expected noise. As with Hartley, the technique proposed in the Timberg article applies a limited characterization of imaging noise to the problem. In the method proposed, noise is calculated using a simple linear scaling of NPS based on two captured flat-field reference images.
The rather simplistic characterizations of noise presented in the previously listed references may be suitable for some individual cases, for example for objects with limited exposure latitude. However, for imaging related to living tissue, noise has proven to be more complex and a more comprehensive solution is needed. For example, the linear scaling noise model assumes that the imaging system is quantum-limited, that is, dominated by the noise of the incoming X-rays, shown to have a Poisson distribution. This assumption is made in the Hartley, Massoumzadeh, and Timberg methods. However, it has been found that the noise power spectrum (NPS) for radiation images is more complex and that image noise has multiple components, including a number of components that exhibit non-linear response to dose. The response that is proportionate to dosage is only one factor for consideration.
While a simple model may be suitable in comparing image noise against a threshold value, however, it is not suited for producing accurate low-dose image simulations from a higher-dose set of images. As a result, the type of solution proposed in Hartley, Massoumzadeh, or Timberg can be unsuitable in cases where a test image has been captured at low dosage levels, images have a large exposure latitude as, for example, in chest radiography, or where electronic noise of the detector is otherwise a significant factor.
Thus, it can be appreciated that there is a need for a simulation method that obtains its results by more accurately profiling system noise and accordingly adapts for noise impact on images captured at various dosage levels.